BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus

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Title: BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus
Authors: Staunton, Patrick M.Miranda-CasoLuengo, Aleksandra A.Loftus, Brendan J.Gormley, Isobel Claire
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Date: 10-Sep-2019
Online since: 2019-11-01T15:25:28Z
Abstract: Background: Although many of the genic features in Mycobacterium abscessus have been fully validated, a comprehensive understanding of the regulatory elements remains lacking. Moreover, there is little understanding of how the organism regulates its transcriptomic profile, enabling cells to survive in hostile environments. Here, to computationally infer the gene regulatory network for Mycobacterium abscessus we propose a novel statistical computational modelling approach: BayesIan gene regulatory Networks inferreD via gene coExpression and compaRative genomics (BINDER). In tandem with derived experimental coexpression data, the property of genomic conservation is exploited to probabilistically infer a gene regulatory network in Mycobacterium abscessus.Inference on regulatory interactions is conducted by combining ‘primary’ and ‘auxiliary’ data strata. The data forming the primary and auxiliary strata are derived from RNA-seq experiments and sequence information in the primary organism Mycobacterium abscessus as well as ChIP-seq data extracted from a related proxy organism Mycobacterium tuberculosis. The primary and auxiliary data are combined in a hierarchical Bayesian framework, informing the apposite bivariate likelihood function and prior distributions respectively. The inferred relationships provide insight to regulon groupings in Mycobacterium abscessus. Results: We implement BINDER on data relating to a collection of 167,280 regulator-target pairs resulting in the identification of 54 regulator-target pairs, across 5 transcription factors, for which there is strong probability of regulatory interaction. Conclusions: The inferred regulatory interactions provide insight to, and a valuable resource for further studies of, transcriptional control in Mycobacterium abscessus, and in the family of Mycobacteriaceae more generally. Further, the developed BINDER framework has broad applicability, useable in settings where computational inference of a gene regulatory network requires integration of data sources derived from both the primary organism of interest and from related proxy organisms.
Funding Details: Science Foundation Ireland
Wellcome Trust
metadata.dc.description.othersponsorship: Insight Research Centre
Type of material: Journal Article
Publisher: Springer
Journal: BMC Bioinformatics
Volume: 20
Copyright (published version): 2019 the Authors
Keywords: Gene regulatory networkMycobacterium abscessusBayesian inferenceData integration
DOI: 10.1186/s12859-019-3042-8
Language: en
Status of Item: Peer reviewed
Appears in Collections:Conway Institute Research Collection
Mathematics and Statistics Research Collection
Medicine Research Collection
Insight Research Collection

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